背景 很少有研究评估人工智能 (AI) 在乳房 X 光检查筛查中的作用。目的 检查分配给后来被诊断患有乳腺癌的女性进行乳房 X 光检查的 AI 风险评分。材料和方法 本回顾性研究使用了 2004 年 1 月至 2019 年 12 月期间进行的检查的图像数据和筛查信息，作为挪威 BreastScreen 的一部分。对后来被诊断患有癌症的女性进行的先前筛查检查，由商用人工智能系统分配人工智能风险评分（分数为1-7，恶性风险低；8-9，中等风险；10，恶性风险高） 。还评估了基于 AI 评分的癌症的乳房 X 线摄影特征。 AI 评分和乳房 X 线摄影特征之间的关联通过双变量测试进行了测试。结果 对 1602 名女性（平均年龄 59 岁 ± 5.1 [SD]）进行的总共 2787 次筛查检查显示，其中 389 名女性（n = 1016）或间隔期（n = 586）癌症的 AI 风险评分为 10（在诊断前的筛查轮次中，乳房 X 光检查分别发现 38.3% 和 231 种癌症（39.4%）。在诊断前两轮筛查（4 年）可获取 AI 评分的筛查检测癌症中，23.0%（531 例中的 122 例）得分为 10。乳腺 X 光检查特征与侵入性筛查检测癌症的 AI 评分相关（P < 0.01）。 001）。筛查检测到的得分为 10 的病例中，有 13.6%（317 例中的 43 例）记录有钙化密度，得分为 1-7 分的病例中，有 4.6%（322 例中的 15 例）记录有钙化密度。结论 超过三分之一的筛查检测和间隔癌症病例在先前筛查时具有最高的 AI 风险评分，这表明在乳房 X 光检查筛查中使用 AI 可能会导致乳腺癌的早期发现。 © RSNA，2023 本文提供补充材料。另请参阅本期梅塔的社论。

Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected (n = 1016) or interval (n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers (P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mehta in this issue.